RESEARCH: INFLUENZA
FOLDING PROJECT #18484 PROFILE
PROJECT TEAM
Manager(s): Dylan NovackInstitution: Temple University
Project URL: View Project Website
WORK UNIT INFO
Atoms: 93,430Core: 0xa8
Status: Public
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TLDR; PROJECT SUMMARY AI BETA
Miniproteins are tiny proteins designed to fight diseases. Scientists want to understand how these miniproteins bind to viruses, like the flu. They're using computer simulations to see how changing a miniprotein's structure affects its ability to grab onto the virus. This research could lead to better ways to design and improve miniprotein drugs.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Designed miniproteins are a class of biomolecules with intermediate sizes—larger than small-molecule drugs, but smaller than monoclonal antibodies.
Miniproteins can be computationally designed to tightly bind protein targets for use as potential therapeutics, a promising new avenue for treating infectious disease. Hemagglutinin is a viral fusion protein that allows H1 influenza A (HA) to bind sialic acid on cell surfaces, as well as being involved in the post-endocytosis mechanism of cellular infection.
The Baker lab at University of Washington has developed de novo designed miniproteins that bind hemagglutinin, and improved their binding through affinity maturation (Chevalier et al.
2017).
Many of the mutations seen in affinity-matured sequences are not found in the binding interface, and it remains an open question how these changes lead to higher affinity.
Furthermore, many of the computational predictions of how single-point mutations affect binding deviate significantly from the experimentally determined values. Could all-atom molecular simulation approaches achieve more accurate predictions? In this set of simulations, we aim to use massively parallel expanded ensemble simulations to predict mutational effects on affinities to hemagglutinin.
By pairing these simulations with other simulations aimed at modeling the binding reactions of these miniproteins to hemagglutinin, we aim to have a relatively complete picture of a miniprotein-target binding reaction and how mutations affect it.
These studies are a large-scale investigation on how miniprotein binding reactions work in atomic detail, towards a better understanding of computational design and modulation of miniprotein therapeutics.
RELATED TERMS GLOSSARY AI BETA
Miniproteins
Small proteins with therapeutic potential.
Miniproteins are engineered proteins smaller than antibodies but larger than small-molecule drugs. They can bind to specific targets in the body, making them promising for treating diseases.
Hemagglutinin
A viral protein that allows influenza A to bind to cells.
Hemagglutinin is a protein found on the surface of the influenza virus. It helps the virus attach to and enter cells by binding to sialic acid molecules on cell surfaces.
Affinity Maturation
Process of increasing the binding strength of a molecule.
Affinity maturation is a technique used to improve the ability of proteins or antibodies to bind to their target molecules. This involves making small changes to the protein's structure through genetic mutations and selecting for those with higher binding affinity.
Molecular Simulation
Using computer models to simulate molecular interactions.
Molecular simulations are computer programs that mimic the behavior of molecules and atoms. They allow researchers to study how molecules interact with each other and their environment, providing insights into biological processes.
Expanded Ensemble Simulations
A type of simulation that explores multiple energy states.
Expanded ensemble simulations are a powerful technique used in molecular dynamics to explore a wider range of possible configurations for a system. This helps researchers to better understand complex phenomena and predict the behavior of molecules under different conditions.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 03:28:13|
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PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 03:28:13|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|---|---|---|---|---|
| 1 | EPYC 7B12 64-CORE | 64 | 17,696 | 1,132,544 | AMD |
| 2 | RYZEN 9 7950X 16-CORE | 32 | 22,592 | 722,944 | AMD |
| 3 | RYZEN 7 7700X 8-CORE | 16 | 38,893 | 622,288 | AMD |
| 4 | 12TH GEN CORE I9-12900K | 24 | 23,125 | 555,000 | Intel |
| 5 | RYZEN 9 5950X 16-CORE | 32 | 14,166 | 453,312 | AMD |
| 6 | 12TH GEN CORE I7-12700K | 20 | 21,492 | 429,840 | Intel |
| 7 | RYZEN 9 5900X 12-CORE | 24 | 16,852 | 404,448 | AMD |
| 8 | RYZEN 7 5800X 8-CORE | 16 | 23,366 | 373,856 | AMD |
| 9 | RYZEN 7 5700X 8-CORE | 16 | 20,975 | 335,600 | AMD |
| 10 | RYZEN 9 3900X 12-CORE | 24 | 13,420 | 322,080 | AMD |
| 11 | RYZEN 7 5700G | 16 | 18,949 | 303,184 | AMD |
| 12 | XEON PLATINUM 8370C CPU @ 2.80GHZ | 16 | 17,278 | 276,448 | Intel |
| 13 | CORE I7-10700K CPU @ 3.80GHZ | 16 | 15,998 | 255,968 | Intel |
| 14 | 12TH GEN CORE I7-12700 | 20 | 12,626 | 252,520 | Intel |
| 15 | 11TH GEN CORE I9-11900K @ 3.50GHZ | 16 | 11,728 | 187,648 | Intel |
| 16 | RYZEN 7 3700X 8-CORE | 16 | 9,820 | 157,120 | AMD |
| 17 | EPYC 7262 8-CORE | 16 | 8,760 | 140,160 | AMD |
| 18 | 11TH GEN CORE I7-11700F @ 2.50GHZ | 16 | 7,571 | 121,136 | Intel |
| 19 | 12TH GEN CORE I7-12700H | 20 | 5,475 | 109,500 | Intel |
| 20 | CORE I7-10700T CPU @ 2.00GHZ | 16 | 5,412 | 86,592 | Intel |